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A Robust Object Tracking Method for Noisy Video using Rough Entropy in Wavelet Domain

  • Anand Singh Jalal
  • Uma Shanker Tiwary

Abstract

In this paper we have proposed a robust object tracking method using rough entropy and flux in wavelet domain. The tracking framework necessitates robust and efficient but accurate methods for segmentation and matching. The object is represented in wavelet domain features to minimize the effect of frame to frame variations and noise. The concept of maximizing rough entropy in wavelet domain helps in finding out the threshold value to make a distinction between the object and the background pixels in a vague situation. The search for the candidate subframe is made fast by using the motion prediction algorithm. A measure based on flux in wavelet domain combined with the number of pixels in the object has been developed. The proposed tracking algorithm yields better results even in noisy video as shown in the experiments. The results show that the wavelet domain segmentation and tracking improves the localization error approximately by 5–7%.

Keywords

Discrete Wavelet Transform Localization Error Object Tracking Wavelet Domain Gabor Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Sonka M., Hiavac V., Boyle R.: Image Processing, Analysis and Machine Vision Thomson Asia Pvt. Ltd., Singapore (2001)Google Scholar
  2. 2.
    Hu W., Tan T., Wang L., Maybank S.: A survey on visual surveillance of object motion and behaviours. IEEE ransactions on Systems. Man and Cybernetics. vol. 34, No. 3 (2004)Google Scholar
  3. 3.
    Yilmaz A., Javed O., Shah M.: Object Tracking: A Survey. ACM Journal of Computing Surveys 38(4) (2006)Google Scholar
  4. 4.
    Comaniciu D., Meer P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. Patt. Analy. Mach. Intell, 603–619 (2002)Google Scholar
  5. 5.
    Shi J., Malik J.: Normalized cuts and image segmentation. IEEE Trans. Patt. Analy. Mach. Intell. 22, 8, 888–905 (2000)CrossRefGoogle Scholar
  6. 6.
    Sankar K. Pal, Uma Shankar B., Pabitra Mitra: Granular computing, rough entropy and object extraction. Pattern Recognition Letters. 26(16):2509–2517 (2005)CrossRefGoogle Scholar
  7. 7.
    Broida T., Chellappa R.: Estimation of object motion parameters from noisy images. IEEE Trans. Patt. Analy. Mach. Intell. 8, 1, 90–99 (1986)CrossRefGoogle Scholar
  8. 8.
    Comaniciu D., Ramesh V., Meer P.: Real-time Tracking of non-rigid objects using Mean Shift. IEEE Conference on Computer Vision and Pattern Recognition. South Carolina, pp. 142–149 (2000)Google Scholar
  9. 9.
    Comaniciu D., Ramesh V.: Mean Shift and optimal prediction for efficient object tracking, Proceedings of the IEEE Int’l Conference on Image Processing (ICIP), pp. 70–73 (2000)Google Scholar
  10. 10.
    Comaniciu D., Ramesh V., Meer P.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (5), pp. 564–575 (2003)CrossRefGoogle Scholar
  11. 11.
    Khansari M., Rabiee H. R., Asadi M., Ghanbari M.: Object tracking in crowded video scenes based on the Undecimated wavelet features and texture analysis. EURASIP Journal on Advances in Signal Processing, Article ID 243534 (2008)Google Scholar
  12. 12.
    He C., Zheng Y. F., Ahalt S. C.: Object tracking using the Gabor wavelet transform and the golden section algorithm. IEEE Transactions on Multimedia, vol. 4, no. 4, pp. 528–538 (2002)CrossRefGoogle Scholar
  13. 13.
    Feris R. S., Krueger V., Cesar Jr. R. M.: A wavelet subspace method for real-time face tracking. Real-Time Imaging, vol. 10, no. 6. pp. 339–350 (2004)CrossRefGoogle Scholar
  14. 14.
    Ashish Khare, Uma Shanker Tiwary: Daubechies complex wavelet transform based moving object tracking. Proceedings of the IEEE Symposium on Computational Intelligence in Image Processing, USA, pp. 36–40, 1–5 April (2007)Google Scholar
  15. 15.
    Yiwei Wang, John F. Doherty, Robert E. Van Duck: Moving object tracking in video. Proceedings of 29th IEEE Int’l Conference on Applied Imagery Pattern Recognition Workshop, pp. 95–101 (2000)Google Scholar
  16. 17.
    Khare A., Tiwary U.: Soft-Thresholding for Denoising of Medical Images: A multiresolution approach. International Journal of Wavelets, Multiresolution and Information Processing (IJWMIP), 3, 477–496, (2005)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Indian Institute of Information Technology, India 2009

Authors and Affiliations

  • Anand Singh Jalal
    • 1
  • Uma Shanker Tiwary
    • 1
  1. 1.Indian Institute of Information TechnologyAllahabadIndia

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